[1] Siegel R, Naishadham D, Jemal A. Cancer statistics,2013 [J]. CA: A Cancer Journal for Clinicians,2013, 63(1): 11-30.[2] Cai W, Lee J-G, Zalis M E, et al. Mosaic decomposition:An electronic cleansing method for inhomogeneously tagged regions in noncathartic CT colonography[J].IEEE Transactions on Medical Imaging, 2011,30(3): 559-574.[3] Liang Z, Yang F, Wax M, et al. Inclusion of a priori information in segmentation of colon lumen for 3D virtual colonoscopy [C]// Proceedings of the Nuclear Science Symposium—Medical Imaging Conference. New York, USA: IEEE, 1998: 1423-1427.[4] Masutani Y, Yoshida H, MacEneaney P M, et al. Automated segmentation of colonic walls for computerized detection of polyps in CT colonography [J]. Journal of Computer Assisted Tomography, 2001,25(4): 629-638.[5] Frimmel H, N¨appi J, Yoshida H. Centerline-based colon segmentation for CT colonography [J]. Medical Physics, 2005, 32(8): 2665-2672.[6] Lu L, Zhang D, Li L, et al. Fully Automated colon segmentation for the computation of complete colon centerline in virtual colonoscopy [J].IEEE Transactions on Biomedical Engineering, 2012, 59(4): 996-1004.[7] Cai W, Lee B, Kim S, et al. Dual-energy electronic cleansing for artifact-free visualization of the colon in fecal-tagging CT colonography [J]. Lecture Notes in Computer Science, 2012, 7029: 8-17.[8] Yoshida H, N¨appi J. Three-dimensional computeraided diagnosis scheme for detection of colonic polyps[J]. IEEE Transactions on Medical Imaging, 2001,20(12): 1261-1274.[9] Wang Z, Liang Z, Li L, et al. Reduction of false positives by internal features for polyp detection in CTbased virtual colonoscopy [J]. Medical Physics, 2005,32(12): 3602-3616.[10] Ong J L, Seghouane A-K. From point to local neighborhood:Polyp detection in CT colonography using geodesic ring neighborhoods [J]. IEEE Transactions on Image Processing, 2011, 20(4): 1000-1010.[11] Yao J, Miller M, Franaszek M, et al. Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models [J]. IEEE Transactions on Medical Imaging, 2004, 23(11): 1344-1352.[12] Zhu H, Duan C, Pickhardt P, et al. Computeraided detection of colonic polyps with level set-based adaptive convolution in volumetric mucosa to advance CT colonography toward a screening modality [J].Cancer Management and Research, 2009, 1: 1-13.[13] N¨appi J J, Nagata K. Sources of false positives in computer-assisted CT colonography [J]. Abdominal Imaging, 2011, 36(2): 153-164.[14] N¨appi J, Yoshida H. Automated detection of polyps with CT colonography: Evaluation of volumetric features for reduction of false-positive findings [J]. Academic Radiology, 2002, 9(4): 386-397.[15] Zhu H, Barish M, Pickhardt P, et al. Haustral fold segmentation with curvature-guided level set evolution[J]. IEEE Transactions on Biomedical Engineering,2013, 60(2): 321-331.[16] Chowdhury A S, Tan S, Yao J, et al. Colonic fold detection from computed tomographic colonography images using diffusion-FCM and level sets [J]. Pattern Recognition Letters, 2010, 31(9): 876-883.[17] Lostumbo A, Suzuki K, Dachman A H. Flat lesions in CT colonography [J]. Abdominal Imaging, 2010,35(5): 578-583.[18] O’Connor S D, Summers R M, Yao J, et al. CT colonography with computer-aided polyp detection:Volume and attenuation thresholds to reduce falsepositive findings owing to the ileocecal valve 7 [J]. Radiology,2006, 241(2): 426-432.[19] Suzuki K, Yoshida H, N¨appi J, et al. Massivetraining artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes [J]. Medical Physics, 2006, 33(10): 3814-3824.[20] G¨okturk S B, Tomasi C, Acar B, et al. A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography [J]. IEEE Transactions on Medical Imaging, 2001, 20(12): 1251-1260.[21] Van Ravesteijn V F, Van Wijk C, Vos F M, et al.Computer-aided detection of polyps in CT colonography using logistic regression [J]. IEEE Transactions on Medical Imaging, 2010, 29(1): 120-131.[22] Huang A, Li J, Summers R M, et al. Improving polyp detection algorithms for CT colonography:Pareto front approach [J]. Pattern Recognition Letters,2010, 31(11): 1461-1469.[23] Yao J, Li J, Summers R M. Employing topographicalheight map in colonic polyp measurement and false positive reduction [J]. Pattern Recognition, 2009,42(6): 1029-1040.[24] Suzuki K. Recent advances in reduction of false positives in computerized detection of polyps in CT colonography [J]. Lecture Notes in Computer Science,2011, 6668: 32-39.[25] Suzuki K, Zhang J, Xu J. Massive-training artificial neural network coupled with Laplacian-eigenfunctionbased dimensionality reduction for computer-aided detection of polyps in CT colonography [J]. IEEE Transactions on Medical Imaging, 2010, 29(11): 1907-1917.[26] Wang G, McFarland E G, Brown B P, et al. GI tract unraveling with curved cross sections [J]. IEEE Transactions on Medical Imaging, 1998, 17(2): 318-322.[27] Yao J, Chowdhury A S, Aman J, et al. Reversible projection technique for colon unfolding [J].IEEE Transactions on Biomedical Engineering, 2010,57(12): 2861-2869.[28] Zhao J, Cao L, Zhuang T, et al. Digital eversion of a hollow structure: An application in virtual colonography[J]. Journal of Biomedical Imaging, 2008, 2008(8):1-6.[29] Zhang D, Zhao J, Lu L, et al. Virtual eversion and rotation of colon based on outer surface centerline [J].Medical Physics, 2010, 37(10): 5518-5529.[30] Kiss G, Van Cleynenbreugel J, Thomeer M, etal. Computer-aided diagnosis in virtual colonography via combination of surface normanl and sphere fitting methods [J]. European Radiologys, 2002, 12(1): 77-81.[31] Summers R M, Yao J, Pickhardt P J, et al.Computed tomographic virtual colonoscopy computeraided polyp detection in a screening population [J].Gastroenterology, 2005, 129(6): 1832-1844.[32] Suzuki K, Yoshida H, N¨appi J, et al. Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography [J]. Medical Physics, 2008,35(2): 694-703.[33] Wang H, Li L, Peng H, et al. A novel computer aided detection (CADe) scheme for colonic polyps based on the structure decomposition [J]. Lecture Notes in Computer Science, 2013, 8198: 63-72.[34] McKenna M T, Wang S, Nguyen T B, et al. Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence [J]. Medical Image Analysis, 2012,16(6): 1280-1292.[35] Wang S, Anugu V, Nguyen T, et al. Fusion of machine intelligence and human intelligence for colonic polyp detection in CT colonography [C]// Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Chicago,Illinois, USA: IEEE, 2011: 160-164.[36] Summers R M. Current concepts in computer-aided detection for ct colonography [C]// Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Rotterdam, the Netherlands: IEEE, 2010: 269-272.[37] Suzuki K. A review of computer-aided diagnosis in thoracic and colonic imaging [J]. Quantitative Imaging in Medicine and Surgery, 2012, 2(3): 163-176.[38] Linguraru M G, Panjwani N, Fletcher J G, et al.Automated image-based colon cleansing for laxativefree CT colonography computer-aided polyp detection[J]. Medical Physics, 2011, 38(12): 6633-6642.[39] N¨appi J, Gryspeerdt S, Lefere P, et al. Automated detection of colorectal lesions in non-cathartic CT colonography [J]. Lecture Notes in Computer Science,2012, 7029: 68-75.[40] N¨appi J, Yoshida H. Virtual tagging for laxativefree CT colonography: Pilot evaluation [J]. Medical Physics, 2009, 36(5): 1830-1838.[41] Wang S, Yao J, Liu J, et al. Centerline registration of prone and supine CT colonography scans based on correlation optimized warping and anatomical landmarks [C]// Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.Boston, Massachusetts, USA: IEEE, 2009: 955-958.[42] Wei Z, Wang S, Petrick N, et al. Supine and prone CT colonography registration by matching graphs of teniae coli [C]// Proceedings of the 2012 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Barcelona, Spain: IEEE, 2012: 712-715. |